import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd

df = pd.read_csv('/Users/mong275/Downloads/elec_source_target_to_test_animation.csv')
df = df[df.year >= 2020]
df = df[df.scenario == 'rcp45cooler_ssp3']
df

# Create a list of unique categories (both source and target categories)
unique_categories = pd.concat([df['source'], df['target']]).unique()

print(unique_categories)

['CSP_resource' 'nuclearFuelGenIII' 'nuclearFuelGenII' 'PV_resource'
 'regional biomass' 'regional coal' 'wholesale gas'
 'onshore wind resource' 'offshore wind resource' 'biomass (IGCC CCS)'
 'biomass (IGCC)' 'biomass (conv CCS)' 'biomass (conv)' 'coal (IGCC CCS)'
 'coal (IGCC)' 'coal (conv pul CCS)' 'coal (conv pul)' 'gas (CC CCS)'
 'gas (CC)' 'gas (CT)' 'gas (steam)' 'Gen_III' 'Gen_II_LWR' 'CSP'
 'CSP (dry_hybrid)' 'PV' 'wind_offshore' 'biomass' 'coal' 'gas'
 'geothermal' 'hydro' 'nuclear' 'solar' 'wind' 'electricity'
 'elect_td_ind' 'elect_td_bld' 'elect_td_trn' 'biomass liquids' 'cement'
 'comm cooking' 'comm hot water' 'comm lighting' 'comm non-building'
 'comm office' 'comm other' 'comm refrigeration' 'comm ventilation'
 'industrial energy use' 'oil refining' 'resid clothes dryers'
 'resid clothes washers' 'resid computers' 'resid cooking'
 'resid dishwashers' 'resid freezers' 'resid furnace fans'
 'resid hot water' 'resid lighting' 'resid other' 'resid refrigerators'
 'resid televisions' 'trn_pass' 'trn_pass_road_LDV' 'trn_pass_road_LDV_4W']

# Create a dictionary mapping each category to an index
category_index = {category: idx for idx, category in enumerate(unique_categories)}

print(category_index)

{'CSP_resource': 0, 'nuclearFuelGenIII': 1, 'nuclearFuelGenII': 2, 'PV_resource': 3, 'regional biomass': 4, 'regional coal': 5, 'wholesale gas': 6, 'onshore wind resource': 7, 'offshore wind resource': 8, 'biomass (IGCC CCS)': 9, 'biomass (IGCC)': 10, 'biomass (conv CCS)': 11, 'biomass (conv)': 12, 'coal (IGCC CCS)': 13, 'coal (IGCC)': 14, 'coal (conv pul CCS)': 15, 'coal (conv pul)': 16, 'gas (CC CCS)': 17, 'gas (CC)': 18, 'gas (CT)': 19, 'gas (steam)': 20, 'Gen_III': 21, 'Gen_II_LWR': 22, 'CSP': 23, 'CSP (dry_hybrid)': 24, 'PV': 25, 'wind_offshore': 26, 'biomass': 27, 'coal': 28, 'gas': 29, 'geothermal': 30, 'hydro': 31, 'nuclear': 32, 'solar': 33, 'wind': 34, 'electricity': 35, 'elect_td_ind': 36, 'elect_td_bld': 37, 'elect_td_trn': 38, 'biomass liquids': 39, 'cement': 40, 'comm cooking': 41, 'comm hot water': 42, 'comm lighting': 43, 'comm non-building': 44, 'comm office': 45, 'comm other': 46, 'comm refrigeration': 47, 'comm ventilation': 48, 'industrial energy use': 49, 'oil refining': 50, 'resid clothes dryers': 51, 'resid clothes washers': 52, 'resid computers': 53, 'resid cooking': 54, 'resid dishwashers': 55, 'resid freezers': 56, 'resid furnace fans': 57, 'resid hot water': 58, 'resid lighting': 59, 'resid other': 60, 'resid refrigerators': 61, 'resid televisions': 62, 'trn_pass': 63, 'trn_pass_road_LDV': 64, 'trn_pass_road_LDV_4W': 65}

# Create a list to store the frames for animation
frames = []

# Create Sankey diagrams for each year
for year in df['year'].unique():

    #filter dataframe to year
    year_data = df[df['year'] == year]
    
    # Create the node labels (combine source and target categories) from complete list
    node_labels = list(unique_categories)
    
    # Create the links (flows between source and target categories). Retrieves the corresponding index based on node
    link_sources = year_data['source'].apply(lambda x: category_index[x]).tolist()
    link_targets = year_data['target'].apply(lambda x: category_index[x]).tolist()
    link_values = year_data['value'].tolist()
    
    # Create the Sankey diagram for the current year
    frame = go.Frame(
        data=[go.Sankey(
            node=dict(
                pad=15,
                thickness=20,
                line=dict(color="black", width=0.5),
                label=node_labels
            ),
            link=dict(
                source=link_sources,
                target=link_targets,
                value=link_values
            )
        )],
        name=str(year)
    )
    frames.append(frame)

print(link_sources)

[0, 1, 3, 4, 4, 4, 4, 5, 5, 6, 6, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 19, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 35, 35, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38]

print(link_targets)

[23, 21, 25, 9, 10, 11, 12, 13, 15, 17, 18, 19, 34, 26, 27, 27, 27, 27, 28, 28, 29, 29, 29, 32, 33, 33, 33, 34, 35, 35, 35, 35, 35, 35, 35, 37, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]

print(link_values)

[0.000512338470933, 4.671176361175375, 8.6141058060614, 1.170197810741578, 4.49691581e-07, 3.04859349898987, 3.0536102e-07, 0.583917610509, 0.389283820643, 1.1661039090327, 0.31974179126453, 0.001448577741377, 13.6815111577, 5.824963979, 0.548203142642125, 2.06801278e-07, 1.217616161966965, 1.1430609000000001e-07, 0.3254783039377, 0.208301605116, 0.7978911239175001, 0.21057627312985, 0.000675885736483, 1.555501603665691, 0.000147350976199, 6.507582059e-05, 5.0157824334433, 5.67801168, 1.765819625716458, 0.5337799090537, 1.009143282783833, 0.96966230918, 1.555501603665691, 5.015994860240089, 15.8038972935, 7.50967311, 10.316003216, 1.63581674, 0.0127287424367, 0.0268907413936, 0.11702812448, 0.350499183431, 0.53085950781, 0.69687496, 0.304222139, 1.28873805, 0.30611342327, 0.46277063703, 10.270105075718, 0.00628066980985, 0.17939548172, 0.012072139662, 0.0308075712, 0.14947238659, 0.07072873883, 0.06175670141, 0.0542639937, 1.113498087262, 0.08151715107, 1.19606613, 0.28389289079, 0.219095358, 0.06356972111, 0.0232124428, 1.549034906]

# Create the initial Sankey diagram (for the first year)
initial_year = df['year'].min()
initial_year_data = df[df['year'] == initial_year]
initial_sources = initial_year_data['source'].apply(lambda x: category_index[x]).tolist()
initial_targets = initial_year_data['target'].apply(lambda x: category_index[x]).tolist()
initial_values = initial_year_data['value'].tolist()

# Create the figure with the first year's data
fig = go.Figure(
    data=[go.Sankey(
        node=dict(
            pad=15,
            thickness=20,
            line=dict(color="black", width=0.5),
            label=node_labels
        ),
        link=dict(
            source=initial_sources,
            target=initial_targets,
            value=initial_values
        )
    )],
    layout=dict(
        title="Sankey Diagram by Year",
        updatemenus=[dict(
            type="buttons",
            showactive=False,
            buttons=[dict(
                label="Play",
                method="animate",
                args=[None, dict(frame=dict(duration=1000, redraw=True), fromcurrent=True)]
            )]
        )],
        sliders=[dict(
            yanchor="top", xanchor="left",
            currentvalue=dict(font=dict(size=20), visible=True, xanchor="right"),
            transition=dict(duration=300),
            steps=[dict(label=str(year), method="animate", args=[[str(year)], dict(mode="immediate", 
                                                                                   frame=dict(duration=0, redraw=True))]) for year in df['year'].unique()]
        )]
    ),
    frames=frames
)

fig.update_layout(
    autosize=False,
    width=1200,
    height=800,
    margin=dict(l=50, r=50, b=100, t=100, pad=4)
)

fig.show()

Prep Data¶

Prep Unique Node Labels¶

Prep Dictionary of Node and Node Index¶

Build an animation frame for each year¶

Create Animation¶

	scenario	source	target	year	value
5	rcp45cooler_ssp3	CSP_resource	CSP	2020	0.021108
6	rcp45cooler_ssp3	CSP_resource	CSP	2025	0.021111
7	rcp45cooler_ssp3	CSP_resource	CSP	2030	0.021121
8	rcp45cooler_ssp3	CSP_resource	CSP	2035	0.021197
9	rcp45cooler_ssp3	CSP_resource	CSP	2040	0.021341
...	...	...	...	...	...
6856	rcp45cooler_ssp3	elect_td_trn	trn_pass_road_LDV_4W	2080	1.020281
6857	rcp45cooler_ssp3	elect_td_trn	trn_pass_road_LDV_4W	2085	1.116455
6858	rcp45cooler_ssp3	elect_td_trn	trn_pass_road_LDV_4W	2090	1.226127
6859	rcp45cooler_ssp3	elect_td_trn	trn_pass_road_LDV_4W	2095	1.386638
6860	rcp45cooler_ssp3	elect_td_trn	trn_pass_road_LDV_4W	2100	1.549035